{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T16:38:46Z","timestamp":1764175126383,"version":"build-2065373602"},"reference-count":34,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,11,27]],"date-time":"2021-11-27T00:00:00Z","timestamp":1637971200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32071775"],"award-info":[{"award-number":["32071775"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Opening Project of Beijing Modern Industrial New Area Development Research Base in 2021-2022","award":["JD2021001"],"award-info":[{"award-number":["JD2021001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Compared with traditional optical and multispectral remote sensing images, hyperspectral images have hundreds of bands that can provide the possibility of fine classification of the earth\u2019s surface. At the same time, a hyperspectral image is an image that coexists with the spatial and spectral. It has become a hot research topic to combine the spatial spectrum information of the image to classify hyperspectral features. Based on the idea of spatial\u2013spectral classification, this paper proposes a novel hyperspectral image classification method based on a segment forest (SF). Firstly, the first principal component of the image was extracted by the process of principal component analysis (PCA) data dimension reduction, and the data constructed the segment forest after dimension reduction to extract the non-local prior spatial information of the image. Secondly, the images\u2019 initial classification results and probability distribution were obtained using support vector machine (SVM), and the spectral information of the images was extracted. Finally, the segment forest constructed above is used to optimize the initial classification results and obtain the final classification results. In this paper, three domestic and foreign public data sets were selected to verify the segment forest classification. SF effectively improved the classification accuracy of SVM, and the overall accuracy of Salinas was enhanced by 11.16%, WHU-Hi-HongHu by 15.89%, and XiongAn by 19.56%. Then, it was compared with six decision-level improved space spectrum classification methods, including guided filtering (GF), Markov random field (MRF), random walk (RW), minimum spanning tree (MST), MST+, and segment tree (ST). The results show that the segment forest-based hyperspectral image classification improves accuracy and efficiency compared with other algorithms, proving the algorithm\u2019s effectiveness.<\/jats:p>","DOI":"10.3390\/rs13234816","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T01:45:02Z","timestamp":1638323102000},"page":"4816","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Improved Fusion of Spatial Information into Hyperspectral Classification through the Aggregation of Constrained Segment Trees: Segment Forest"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3738-5429","authenticated-orcid":false,"given":"Jianmei","family":"Ling","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China"},{"name":"Engineering Research Center for Forestry-Oriented Intelligent Information Processing, National Forestry and Grassland Administration, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9823-0565","authenticated-orcid":false,"given":"Lu","family":"Li","sequence":"additional","affiliation":[{"name":"School of Automation, Beijing Information Science and Technology University, Beijing 100096, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haiyan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China"},{"name":"Engineering Research Center for Forestry-Oriented Intelligent Information Processing, National Forestry and Grassland Administration, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Poojary, N., D\u2019Souza, H., Puttaswamy, M.R., and Kumar, G.H. (2016, January 13\u201315). Automatic target detection in hyperspectral image processing: A review of algorithms. Proceedings of the International Conference on Fuzzy Systems & Knowledge Discovery, Changsha, China.","DOI":"10.1109\/FSKD.2015.7382255"},{"key":"ref_2","first-page":"4047","article-title":"Decision-level fusion of spectral reflectance and derivative information for robust hyperspectral land cover classification","volume":"48","author":"Kalluri","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Klaus, A., Sormann, M., and Karner, K. (2006, January 20\u201324). Segment-based stereo matching using belief propagation and a self-adapting dissimilarity measure. Proceedings of the 18th International Conference on Pattern Recognition, Hong Kong, China.","DOI":"10.1109\/ICPR.2006.1033"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1080\/01431160600746456","article-title":"A survey of image classification methods and techniques for improving classification performance","volume":"28","author":"LU","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.1109\/TGRS.2004.831865","article-title":"Classification of hyperspectral remote sensing images with support vector machines","volume":"42","author":"Melgani","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2218","DOI":"10.1109\/TGRS.2008.2010404","article-title":"Active learning methods for remote sensing image classification","volume":"47","author":"Tuia","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/BF00048682","article-title":"Multinomial logistic regression algorithm","volume":"44","year":"1992","journal-title":"Ann. Inst. Statal Math."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2271","DOI":"10.1109\/TGRS.2009.2037898","article-title":"Semisupervised neural networks for efficient hyperspectral image classification","volume":"48","author":"Ratle","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1109\/TGRS.2012.2201730","article-title":"Hyperspectral image classification via kernel sparse representation","volume":"51","author":"Yi","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Gittins, R. (1985). Canonical Analysis: A Review with Applications in Ecology, Springer.","DOI":"10.1007\/978-3-642-69878-1"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1109\/LGRS.2006.888109","article-title":"Hyperspectral image compression using JPEG2000 and principal component analysis","volume":"4","author":"Du","year":"2007","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1109\/TFUZZ.2010.2089631","article-title":"LDA-Based Clustering Algorithm and Its Application to an Unsupervised Feature Extraction","volume":"19","author":"Li","year":"2011","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"676","DOI":"10.1109\/JPROC.2012.2229082","article-title":"Feature mining for hyperspectral image classification","volume":"101","author":"Jia","year":"2013","journal-title":"Proc. IEEE"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Vandenbroucke, N., and Porebski, A. (2021). Multi color channel vs. multi spectral band representations for texture classification. Pattern Recognition. ICPR International Workshops and Challenges, Springer.","DOI":"10.1007\/978-3-030-68790-8_25"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"810002","DOI":"10.3788\/gzxb20144308.0810002","article-title":"Based on texture feature and extend morphological profile fusion for hyperspectral image classification","volume":"43","author":"Bo","year":"2014","journal-title":"Acta Photonica Sin."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"7738","DOI":"10.1109\/TGRS.2014.2318058","article-title":"Spectral\u2013spatial hyperspectral image classification via multiscale adaptive sparse representation","volume":"52","author":"Fang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3681","DOI":"10.1109\/TGRS.2014.2381602","article-title":"Local binary patterns and extreme learning machine for hyperspectral imagery classification","volume":"53","author":"Li","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2666","DOI":"10.1109\/TGRS.2013.2264508","article-title":"Spectral\u2013spatial hyperspectral image classification with edge-preserving filtering","volume":"52","author":"Kang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1109\/LGRS.2013.2250905","article-title":"Hyperspectral image classification using gaussian mixture models and markov random fields","volume":"11","author":"Li","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","unstructured":"Grady, L. (2005, January 20\u201326). Multilabel random walker image segmentation using prior models. Proceedings of the Computer Vision and Pattern Recognition (CVPR 2005), San Diego, CA, USA."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Makantasis, K., Karantzalos, K., Doulamis, A., and Doulamis, N. (2015, January 26\u201331). Deep supervised learning for hyperspectral data classification through convolutional neural networks. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326945"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Patel, H., and Upla, K.P. (2021). A shallow network for hyperspectral image classification using an autoencoder with convolutional neural network. Multimedia Tools and Applications, Springer.","DOI":"10.1007\/s11042-021-11422-w"},{"key":"ref_23","first-page":"108224","article-title":"Deep neural networks-based relevant latent representation learning for hyperspectral image classification","volume":"121","author":"As","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2354","DOI":"10.1109\/TIP.2018.2799324","article-title":"Hyperspectral image classification with markov random fields and a convolutional neural network","volume":"27","author":"Cao","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Yao, X., Li, G., Xia, J., Jin, B., and Zhu, D. (2019). Enabling the big earth observation data via cloud computing and dggs: Opportunities and challenges. Remote Sens., 12.","DOI":"10.3390\/rs12010062"},{"key":"ref_26","first-page":"102485","article-title":"Large-scale crop mapping from multi-source optical satellite imageries using machine learning with discrete grids","volume":"103","author":"Shuai","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"724","DOI":"10.1137\/0205051","article-title":"Finding minimum spanning trees","volume":"5","author":"Cheriton","year":"1976","journal-title":"Siam J. Comput."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Li, L., Wang, C., Chen, J., and Ma, J. (2017). Refinement of hyperspectral image classification with segment-tree filtering. Remote Sens., 9.","DOI":"10.3390\/rs9010069"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"112012","DOI":"10.1016\/j.rse.2020.112012","article-title":"WHU-Hi: UAV-borne hyperspectral with high spatial resolution (H2) benchmark datasets and classifier for precise crop identification based on deep convolutional neural network with CRF\u2014ScienceDirect","volume":"250","author":"Yza","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_30","first-page":"10","article-title":"Aerial hyperspectral remote sensing classification dataset of Xiongan New Area(Matiwan Village)","volume":"24","author":"Cen","year":"2020","journal-title":"J. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1109\/LGRS.2005.846011","article-title":"On the impact of PCA dimension reduction for hyperspectral detection of difficult targets","volume":"2","author":"Farrell","year":"2005","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_32","unstructured":"Cao, P. (2013). Automatic Segmentation Algorithm for Magnetic Resonance Imaging Based on Improved MRF Parameter Estimation, Zhejiang University."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Sun, H., and Wang, W. (2009, January 6\u20138). A new algorithm for unsupervised image segmentation based on D-MRF model and ANOVA. Proceedings of the IC-NIDC 2009, IEEE International Conference on Network Infrastructure and Digital Content, Beijing, China.","DOI":"10.1109\/ICNIDC.2009.5360817"},{"key":"ref_34","unstructured":"(2021, November 08). Eigen. Available online: https:\/\/eigen.tuxfamily.org\/index.php?title=Main_Page."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/23\/4816\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:36:40Z","timestamp":1760168200000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/23\/4816"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,27]]},"references-count":34,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["rs13234816"],"URL":"https:\/\/doi.org\/10.3390\/rs13234816","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2021,11,27]]}}}